1. Field
The technology of the present application relates generally to neurological feedback systems, and more specifically to using neurological systems to control physical objects for therapeutic and other reasons.
2. Background
Biofeedback has been a known conditioning and therapeutic technique for years. Generally, the treatment and training provides a sensor to identify or monitor a physiological condition, such as electrodermal or galvanic skin response (EDR or GSR), heart rate variability (HRV), or the like, and a display, such as a computer or television screen. A control processor receives the sensor information and, in many simplistic devices, adjusts the displayed response to encourage an appropriate response. For example, a resting HRV may provide a balloon image on a display floating high above a landscape. As the patient's or subject's HRV becomes more indicative of agitation, the balloon may fall closer to the horizon on the display.
Another type of biofeedback relates to the electrical functioning of the brain and is generally referred to as neurofeedback. Brain functions generate electrical signals (brainwaves) that may be sensed by electrodes typically in close proximity to the skull. Controlling the brainwaves or conditioning the brain to produce certain types of brainwaves is believed to facilitate wellness. In this regard, neurofeedback is of particular interest to psychologists and the like as the brain is the central organ that controls emotions, physical actions, thoughts, and behaviors. Thus, it is believed that influencing the brain to produce particular types of brainwaves may facilitate corrective training for various disorders, such as, for example, anxiety, depression, attention deficit and hyperactivity disorder, Asperger's syndrome, obsessive compulsive disorder, and the like.
Using an electroencephalograph (EEG) in conjunction with computer processors allows precise and fast determination of brainwave activity. Generally, brainwaves are classified into five generic categories:
1. Brainwaves having a frequency of 2 to 4 hertz are generally referred to as Delta waves;
2. Brainwaves having a frequency of 4 to 8 hertz are generally referred to as Theta waves;
3. Brainwaves having a frequency of 8 to 12 hertz are generally referred to as Alpha waves; and
4. Brainwaves having a frequency of 12 to 26 hertz are generally referred to as Beta waves; and
5. Brainwaves having a frequency of 26 to 50 hertz are generally referred to as Gamma waves.
These brainwaves are measured by sensing the electrical signals of the brain using electrodes. The electrical signals sensed by the electrodes are passed through band pass filters to isolate brainwaves in particular frequency ranges to establish the Delta, Theta, Alpha, Beta and Gamma wave sets. Typically, Delta waves are associated with deep sleep brain activity. Theta waves are often associated with a transitional phase between sleep and wakefulness. Alpha waves are typically associated with periods relaxation, such as, for example, meditation. Beta waves are associated with concentration and task specific activities.
One popular way to analyze brainwaves is to measure the coherence or discoherence (also referred to as non-coherence and incoherence) between the brainwaves from various portions of the brain in a particular frequency range. For example, a person's Alpha waves from two different regions of the brain can be compared. The degree of similarity between the Alpha waves of the two regions would determine the degree of coherence.
Not uncommon today, a person undergoing neurofeedback treatment uses a video display to provide real-time visual feedback in response to detected brainwave activity. A computer processes the brainwaves and compares the actual brainwave to a normalized or desired pattern. The “closeness” to the normalized or desired pattern provides an input to the processor controlling the video display. Based on the desired activity, the video may behave in a pattern to induce the user to alter the user's brainwave. Thus, the visual feedback can be used to condition the brain to produce a desired brainwave pattern matched to a normalized or desired brainwave pattern. Recently, a trend has existed that uses the information to control video in simulations more akin to game playing than therapeutic exercises. Today's technology regarding using brainwaves in neurofeedback therapeutic devices and/or game playing, however, is relatively unsatisfactory. In particular, due in part to the rapidity that brainwaves change, the brainwave coherence values provide at best an unstable signal to control video. Moreover, the instability of the signal makes it difficult or virtually impossible to control a physical device based on brainwave activity. However, for at least improved therapeutic results, it would be preferable to provide a real vs. virtual visual feedback mechanism. Moreover, for both game playing and therapeutic systems, brainwaves typically change overly rapidly for a fine-tuned control, thus today's systems generally have only sluggish, course controls that are unsatisfactory for either games or therapies.
Thus, against this background, it would be desirous to provide a method and system of using brainwaves to provide improved therapeutic and game-playing controls.